This project explores the implementation of a basic Simultaneous Localization and Mapping (SLAM) algorithm within a simulated environment. SLAM enables an agent to create a map of its surroundings while estimating its own position, a capability critical for applications in autonomous vehicles, drones, and robotic navigation systems.
The project is divided into two main components:
- Feature Detection: Uses the Seeded Region Growing algorithm to detect and extract features in the environment.
- Localization: Employs the Extended Kalman Filter (EKF) to estimate the agent's position based on measurements from the feature detection module.
The project is developed in Python, using Pygame for visualization and various scientific libraries for processing. The agent navigates a virtual space, incrementally updating its position estimate and mapping its environment.
- Successfully achieved localization of the agent within the simulated environment.
- Encountered limitations in fully integrating the feature detection and localization components.
This project serves as a foundational implementation of SLAM, with potential for further enhancements in feature detection and data association techniques.